A first shot at TMVA for Chargino Analysis
A. Muennich
CERN
A. Muennich
A first shot at TMVA for Chargino Analysis
1
Inputs
Signal:
−
+
− 0 0
e+ e− → χ̃+
1 χ̃1 → W W χ̃1 χ̃1
ProdID = 249
Background
e+ e− → WW νν
+ −
+ −
+ −
e e → ẽ ẽ →
ProdID = 246
e e → ZZ νν
ProdID = 247
W W − χ̃01 χ̃01 νν
+ −
ProdID = 260
+
e e → qqHνν
A. Muennich
ProdID = 277
A first shot at TMVA for Chargino Analysis
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Disclaimer
This is just a status update
All plots are more than preliminary
Still in the process of understanding TMVA
Signal and background may not be weighted correctly
Some backgrounds still missing
Not enough statistics
... probably many more bugs
A. Muennich
A first shot at TMVA for Chargino Analysis
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Input variables
A. Muennich
A first shot at TMVA for Chargino Analysis
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Correlation of input variables
A. Muennich
A first shot at TMVA for Chargino Analysis
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Background Rejection
A. Muennich
A first shot at TMVA for Chargino Analysis
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TMVA Outputs
A. Muennich
A first shot at TMVA for Chargino Analysis
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Performance of the method
A. Muennich
A first shot at TMVA for Chargino Analysis
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Using the Classifier (300 fb−1 )
ProcID==0 selects true signal.
A. Muennich
A first shot at TMVA for Chargino Analysis
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Compare Classifiers
A. Muennich
A first shot at TMVA for Chargino Analysis
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Efficiency for Likelihood> 0.8
Goal: Tune cuts so that efficiency for signal becomes flat and
maximal and efficiency for background minimal
A. Muennich
A first shot at TMVA for Chargino Analysis
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Combine Classifiers
MLPBNN>0.2 && BDTG>-0.4 && Likelihood>0.4
A. Muennich
A first shot at TMVA for Chargino Analysis
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Efficiency
MLPBNN>0.2 && BDTG>-0.4 && Likelihood>0.4
Goal: Tune cuts so that efficiency for signal becomes flat and
maximal and efficiency for background minimal
A. Muennich
A first shot at TMVA for Chargino Analysis
13